*A minimum purchase of $35 is required. Shipping is provided via FedEx SmartPost® and FedEx Express Saver®. Average delivery time is 1 – 5 business days, but is not guaranteed in that timeframe. Also allow 1 - 2 days for processing. Free shipping is eligible only in the continental United States and excludes Hawaii, Alaska and Puerto Rico. FedEx service marks used by permission."Marketplace" orders are not eligible for free or discounted shipping.

30 day, 100% satisfaction guarantee

If an item you ordered from TextbookRush does not meet your expectations due to an error on our part, simply fill out a return request and then return it by mail within 30 days of ordering it for a full refund of item cost.

Description: Greatly expanded and revised this second edition of Bayesian Statistics includes a large number of applications to support the usefulness of the subject matter and a discussion of the rare topic of the De Finetti Transform.

Greatly expanded and revised this second edition of Bayesian Statistics includes a large number of applications to support the usefulness of the subject matter and a discussion of the rare topic of the De Finetti Transform.

Preface

Preface to the First Edition

A Bayesian Hall of Fame

Foundations and Principles

Background

Rationale for Bayesian Inference and Preliminary Views of Bayes' Theorem

Example: Observing a Desired Experimental Effect

Thomas Bayes

Brief Descriptions of the Chapters

A Bayesian Perspective on Probability

Introduction

Types of Probability

Coherence

Operationalizing Subjective Probability Beliefs

Calibration of Probability Assessors

Comparing Probability Definitions

The Likelihood Function

Introduction

Likelihood Function

Likelihood Principle

Likelihood Principle and Conditioning

Likelihood and Bayesian Inference

Development of the Likelihood Function Using Histograms and Other Graphical Methods

Bayes' Theorem

Introduction

General Form of Bayes' Theorem for Events

Bayes' Theorem for Discrete Data and Discrete Parameter

Bayes' Theorem for Continuous Data and Discrete Parameter

Bayes' Theorem for Discrete Data and Continuous Parameter

Bayes' Theorem for Continuous Data and Continuous Parameter

Prior Distributions

Introduction

Objective and Subjective Prior Distributions

(Univariate) Prior Distributions for a Single Parameter

Prior Distributions for Vector and Matrix Parameters

Data-Mining Priors

Wrong Priors

Numerical Implementation of the Bayesian Paradigm

Markov Chain Monte Carlo Methods

Introduction

Metropolis-Hastings (M-H) Algorithm

Multiple-Block M-H Algorithm

Some Techniques Useful in MCMC Sampling

Examples

Comparing Models Using MCMC Methods

Large Sample Posterior Distributions and Approximations

Introduction

Large-Sample Posterior Distributions

Approximate Evaluation of Bayesian Integrals

Importance Sampling

Bayesian Statistical Inference and Decision Making

Bayesian Estimation

Introduction

Univariate (Point) Bayesian Estimation

Multivariate (Point) Bayesian Estimation

Interval Estimation

Empirical Bayes' Estimation

Robustness in Bayesian Estimation

Bayesian Hypothesis Testing

Introduction

A Brief History of Scientific Hypothesis Testing

Problems with Frequentist Methods of Hypothesis Testing

Lindley's Vague Prior Procedure for Bayesian Hypothesis Testing

Jeffreys' Procedure for Bayesian Hypothesis Testing

Predictivism

Introduction

Philosophy of Predictivism

Predictive Distributions/Comparing Theories

Exchangeability

De Finetti's Theorem

The De Finetti Transform

Predictive Distributions in Classification and Spatial and Temporal Analysis

Bayesian Neural Nets

Bayesian Decision Making

Introduction

Loss Functions

Admissibility

Models and Applications

Bayesian Inference in the General Linear Model

Introduction

Simple Linear Regression

Multivariate Regression Model

Multivariate Analysis of Variance Model

Bayesian Inference in the Multivariate Mixed Model

Model Averaging

Introduction

Model Averaging and Subset Selection in Linear Regression

Prior Distributions

Posterior Distributions

Choice of Hyperparameters

Implementing BMA

Examples

Hierarchical Bayesian Modeling

Introduction

Fundamental Concepts and Nomenclature

Applications and Examples

Inference in Hierarchical Models

Relationship to Non-Bayesian Approaches

Computation for Hierarchical Models

Software for Hierarchical Models

Bayesian Factor Analysis

Introduction

Background

Bayesian Factor Analysis Model for Fixed Number of Factors

Choosing the Number of Factors

Additional Model Considerations

Bayesian Inference in Classification and Discrimination

Introduction

Likelihood Function

Prior Density

Posterior Density

Predictive Density

Posterior Classification Probability

Example: Two Populations

Second Guessing Undecided Respondents: An Application

Extensions of the Basic Classification Problem

Description of Appendices

Bayes, Thomas

Thomas Bayes. A Bibliographical Note

Communication of Bayes' Essay to the Philosophical Transactions of the Royal Society of London

*A minimum purchase of $35 is required. Shipping is provided via FedEx SmartPost®
and FedEx Express Saver®. Average delivery time is 1 – 5 business days, but
is not guaranteed in that timeframe. Also allow 1 - 2 days for processing. Free shipping is eligible only in the continental United States and excludes
Hawaii, Alaska and Puerto Rico. FedEx service marks used by permission."Marketplace"
orders are not eligible for free or discounted shipping.